Deep Convolutional Neural Network Final Year Projects with Source Code

Byadmin Final Year Projects

Deep Convolutional Neural Network Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Deep Convolutional Neural Network projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Deep Convolutional Neural Network Final Year Projects

  1. A Comprehensive Joint Learning System to Detect Skin Cancer
    This project builds a smart system that helps doctors detect skin diseases early. It studies images of the skin and learns patterns that indicate different types of diseases. The system combines two learning methods to improve accuracy. It achieves very high accuracy in identifying multiple skin conditions.
  2. A Review on Alzheimers Disease Through Analysis of MRI Images Using Deep Learning Techniques
    This project focuses on using brain MRI scans to detect Alzheimer’s disease early. It applies deep learning, especially convolutional neural networks, to analyze brain structures and identify signs of the disease. By examining the detailed tissue patterns, the method aims to improve accuracy in diagnosing Alzheimer’s. The study also reviews recent research and techniques showing how MRI segmentation helps in early detection.
  3. Automatic Liver Cancer Detection Using Deep Convolution Neural Network
    This project focuses on automatically detecting liver cancer from CT scans. It uses a new method called ESP-UNet to accurately separate the liver from the rest of the image, avoiding errors in segmentation. After that, a lightweight deep learning model analyzes the segmented liver to detect cancer. The method shows better results than previous approaches in terms of accuracy and reliability.
  4. EfficientNetB3-Adaptive Augmented Deep Learning AADL for Multi-Class Plant Disease Classification
    This project focuses on automatically identifying plant diseases using artificial intelligence. It uses advanced deep learning models that have already been trained on large datasets to recognize 52 types of diseases and healthy leaves. The study tested several models and found that one called EfficientNetB3-AADL gave the most accurate results, correctly identifying diseases 98.7% of the time. This approach can help farmers quickly and accurately detect plant diseases to protect crops.
  5. Computer Aided Diagnosis for Gastrointestinal Cancer Classification Using Hybrid Rice Optimization With Deep Learning
    This project aims to detect stomach and digestive cancers early using computer analysis of medical images. It cleans the images and then uses advanced AI models to learn important patterns. The system chooses the best settings automatically to improve accuracy. This helps doctors identify cancer sooner and make better treatment decisions.
  6. Conditional Generative Adversarial Network Model for Conversion of 2 Dimensional Radiographs into 3 Dimensional Views
    This project develops a method to convert 2-D medical images like X-rays into 3-D views. It uses a specialized deep learning model that can show the organ from all angles. The system cleans and standardizes the images before processing, and it is designed to work even with noisy or unclear inputs. Tests on real hospital data show that the generated 3-D images preserve important details and match the quality of the original scans.
  7. Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images
    This project focuses on automatically detecting diseases in the gastrointestinal tract using images from a tiny camera capsule. The researchers developed a computer program that cleans the images, extracts important features, and classifies diseases like bleeding, ulcers, and polyps. They combined advanced deep learning techniques with optimization algorithms to improve accuracy. Tests on a medical image database showed the system can correctly identify diseases with over 98% accuracy.
  8. Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption
    This project focuses on using machine learning to identify and classify eye diseases. The researchers designed a new neural network model that works efficiently without using too much memory. They tested it on a dataset containing 32 types of retinal diseases. The model performed very well, achieving 95% accuracy while managing resources better than previous methods.
  9. Oppositional Jellyfish Search Optimizer With Deep Transfer Learning Enabled Secure Content-Based Biomedical Image Retrieval
    This project focuses on securely storing and retrieving medical images like X-rays, MRIs, and CT scans. It uses advanced deep learning techniques to extract important features from the images. The system encrypts images to keep them safe while allowing accurate searching and matching. Tests show that this method works better than existing approaches.
  10. A Smart Leaf Blow Robot Based on Deep Learning Model
    This project created a robot that can automatically collect fallen leaves. It uses a camera and a computer program to recognize leaves on the ground. The robot moves on wheels and directs a blower to gather the leaves into a bag. The system works in real time and can handle different types of leaves without human help.
  11. Deep Learning Using Context Vectors to Identify Implicit Aspects
    This project focuses on finding the hidden topics that people talk about in their reviews. It looks for meanings that are not directly written but are implied through the words people use. The system learns from examples and understands the surrounding text to detect these hidden ideas. It helps improve sentiment analysis by making it more accurate and closer to real human understanding.
  12. Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning
    This project focuses on detecting a dangerous electrical problem called chaotic ferroresonance, which can cause high voltages and damage equipment. The researchers used deep learning models to quickly identify when this problem occurs. They trained the models on images of voltage patterns and achieved high accuracy. This helps power networks respond faster and protect equipment from damage.
  13. End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model
    This project focuses on automatically reading Tamil handwritten text and converting it into digital text. It uses deep learning to first improve image quality and then separate lines and words. A MobileNet-based model extracts features, and a BiGRU model with optimization identifies each character. Tests show it can recognize Tamil handwriting accurately, achieving nearly 98.5% accuracy.
  14. MLFAN Multilevel Feature Attention Network With Texture Prior for Image Denoising
    This project focuses on improving image denoising using deep learning. The researchers developed a new convolutional neural network that considers texture details and multiple levels of image features. Their model uses attention mechanisms to focus on the most important features for better noise removal. Experiments show it removes noise more effectively than many existing methods, keeping image details clear.
  15. Multi-FusNet of Cross Channel Network for Image Super-Resolution
    This project focuses on improving image quality using artificial intelligence. It develops a new method called MFCC that makes low-resolution images clearer and sharper. The approach is faster and uses fewer resources than existing methods while keeping the images visually high quality. Tests show it outperforms current techniques in accuracy and efficiency.
  16. SENext Squeeze-and-ExcitationNext for Single Image Super-Resolution
    This project focuses on improving low-resolution images to high-resolution images using a deep learning method. The researchers designed a new network called SENext, which reduces computation and memory needs while keeping high image quality. It uses special blocks to enhance important features and skip connections to reuse information efficiently. Tests show that SENext is faster, uses fewer resources, and produces sharper and clearer images compared to existing methods.
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